Abstract: Aspects of the present disclosure involve a system and a method for performing operations comprising: receiving a two-dimensional continuous surface representation of a three-dimensional object, the continuous surface comprising a plurality of landmark locations; determining a first set of soft membership functions based on a relative location of points in the two-dimensional continuous surface representation and the landmark locations; receiving a two-dimensional input image, the input image comprising an image of the object; extracting a plurality of features from the input image using a feature recognition model; generating an encoded.

Abstract: The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

Abstract: Disclosed here is a method of matching color and appearance of coatings containing at least one effect pigment. The method involves: obtaining, for a coating sample to be matched, sample color image data having pixel-specific color model properties; creating local binary patterns based on the pixel-specific color model properties; generating a binary pattern histogram for the local binary patterns, wherein the binary pattern histogram arranges the local binary patterns into N distinct bins to obtain N bin counts, and wherein the binary pattern histogram represents an appearance feature descriptor of the coating sample to be matched; and utilizing the binary pattern histogram for comparison against a previously generated binary pattern histogram that serves as an appearance feature descriptor of a candidate coating specimen.

Abstract: A method for multi-label image classification in a convolutional neural network (CNN) is provided that includes forming a composite image from a plurality of clipped images, and processing the composite image by the CNN to generate a probability vector for each clipped image of the plurality of clipped images, wherein a length of a probability vector is equal to a number of classes the CNN is designed to classify.

Abstract: Aspects of the present invention provide methods, systems, and/or the like for: (1) determining temperature data for at least part of an article of manufacture in a manufacturing process; (2) analyzing the temperature data to identify a first portion of the article of manufacture having a temperature that deviates from a surrounding portion of the first portion, the first portion comprising: at least one hot spot, at least one hot streak, at least one cold spot, or at least one cold streak on the article of manufacture; (3) determining, for the first portion of the article of manufacture, a set of properties related to the temperature: (4) processing the set of properties using at least one of a rules-based model, a machine-learning model, or a classification model to produce a defect classification; and (5) providing an indication of the defect classification for display on a computing device.

Abstract: Disclosed here is a method of matching color and appearance of coatings containing at least one effect pigment. The method involves: obtaining, for a coating sample, sample color image data having pixel-specific color model properties; generating a characterizing histogram for the sample color image data, wherein the characterizing histogram represents an appearance feature descriptor of the coating sample; creating a sample histogram vector from the characterizing histogram; retrieving a specimen histogram vector for a candidate coating specimen; and calculating a cosine similarity value from the sample histogram vector and the specimen histogram vector, wherein the cosine similarity value indicates how well the candidate coating specimen matches the coating sample to be matched.

Abstract: Systems and methods for classification and/or analysis of skin color(s) are provided. The technology integrates artificial intelligence (AI) with dermatology to enhance skin color classification. The unique matching technique aligns skin images with the Fitzpatrick skin type palette through Euclidean distance matching, achieving higher precision in skin color classification compared to conventional methods.

Abstract: A system for automated image analysis includes an imaging device configured to capture input data of a patient, a processor, and a memory coupled to the processor. The memory has instructions stored thereon, which when executed by the processor, cause the system to: capture input data from the imaging device, the input data including a three-dimensional (3D) image of a portion of a patient; receive the input data from the imaging device; generate a first 3D mask based on the input data; generate a second 3D mask based on the first 3D mask; and determine a diagnostic prediction based on the first 3D mask and the second 3D mask. Generating the second 3D mask includes predicting a plurality of two-dimensional (2D) slices based on the first 3D mask, such that each 2D slice of the plurality of 2D slices is an axial cross-section of the first 3D mask.

Abstract: A computing device includes: a tomographic image acquisition unit that acquires a plurality of tomographic images representing shapes of reinforcing fibers and shapes of resin in a plurality of cross sections obtained when an article containing the reinforcing fibers and the resin is divided in parallel; and a computing unit that calculates a rigidity parameter of each of a plurality of small regions obtained by dividing the article, based on the plurality of acquired tomographic images.

Abstract: A method is disclosed. The method includes providing an imaging apparatus, recording image data of an imaging location using the imaging apparatus, displaying the image data to a user via a user device, selecting an image object from the image data based on a selection criteria, and determining whether or not a selection criteria error of the image object is to be checked. The method also includes displaying a bounding shape, which bounds the image object, to the user via the user device when the selection criteria error is to be checked, prompting the user to enter user input indicating whether or not the selection criteria error is present, and storing data of the image object in a cache when the user input indicates that the selection criteria error is present.

Abstract: A method of operation of a compute system includes: receiving patient images by an head ailment artificial intelligence (AI) including a multi-task decoder block; concurrently generating a scalp segmentation and a hair loss heat map from the multi-task decoder block of the head ailment AI; generating a composite hair loss image based on the hair loss heat map and a quadrant computation of the scalp segmentation; and generating a SALT score based the hair loss heat map and the composite hair loss image for displaying on a device.

Abstract: A computer-implemented method for preventing unauthorized processing of a digital representation of at least a portion of a document, a device for data processing, and a computer program product are provided, wherein in particular the document is a banknote. The method comprises providing data, wherein the data is based on the digital representation of at least a portion of a test element. The digital representation may be an image file corresponding to the at least one portion of the test element. The method also involves analyzing the data with regard to data representing at least one characterizing feature of the at least one portion of the document. The method further comprises activating prohibiting means if the data being based on the digital representation of the at least one portion of the test element is similar to the data representing the at least one characterizing feature.

Abstract: The present disclosure relates to utilizing a style-matching image generation system to generate large datasets of style-matching images having matching styles and content to an initial small sample set of input images. For example, the style-matching image generation system utilizes a selection of style-mixed stored images with a generative machine-learning model to produce large datasets of synthesized images. Further, the style-matching image generation system utilizes the generative machine-learning model to conditionally sample synthesized images that accurately match the style, content, characteristics, and patterns of the initial small sample set and that also provide added variety and diversity to the large image dataset.

Abstract: A method for analyzing dementia severity, may comprise calculating color data from skin regions of interest of a frontal lobe region; calculating frequency values corresponding to a frequency band of a dementia-related biosignal based on the calculated color data; and analyzing dementia severity using the calculated frequency values.

Abstract: A method for automatically adjusting camera parameters to improve video analytics accuracy during continuously changing environmental conditions is presented. The method includes capturing a video stream from a plurality of cameras, performing video analytics tasks on the video stream, the video analytics tasks defined as analytics units (AUs), applying image processing to the video stream to obtain processed frames, filtering the processed frames through a filter to discard low-quality frames and dynamically fine-tuning parameters of the plurality of cameras. The fine-tuning includes passing the filtered frames to an AU-specific proxy quality evaluator, employing State-Action-Reward-State-Action (SARSA) reinforcement learning (RL) computations to automatically fine-tune the parameters of the plurality of cameras, and based on the reinforcement computations, applying a new policy for an agent to take actions and learn to maximize a reward.

Abstract: An ultrasound diagnosis apparatus according to an embodiment includes processing circuitry. The processing circuitry is configured to receive an input of first ultrasound data and to output second ultrasound data. The processing circuitry is configured to reconstruct third ultrasound data on the basis of amplitude information in the second ultrasound data and phase information in the first ultrasound data. The processing circuitry is configured to perform a process that uses amplitude information and phase information in the third ultrasound data.

Abstract: In some implementations, a device includes one or more processors and a non-transitory memory. In some implementations, a method includes obtaining an object that includes a plurality of portions including a first portion and a second portion. In some implementations, the method includes determining a first saliency value for the first portion of the object and a second saliency value for the second portion of the object. In some implementations, the method includes triggering presentation of the first portion of the object in accordance with a first display characteristic that is based on the first saliency value and the second portion of the object in accordance with a second display characteristic that is based on the second saliency value.

Abstract: Systems and methods for increasing a false positive identification rate of a face recognition algorithm are provided. A method includes using a first image sensor, acquiring image data for a first facial image of a person and selecting an application profile for the first facial image. The application profile is selected to modify the image data to alter a value of an image classification feature used by a face recognition algorithm to increase a false positive identification rate of the face recognition algorithm that is configured to match an input facial image with at least one of N stored facial images. The method further includes, modifying an aspect associated with the face of the person, such that a second facial image of the face, when acquired by a second image sensor subsequent to the modification of the aspect, is different from the first facial image.

Abstract: Apparatuses, systems and methods are provided for generating a vehicle driver signature. More particularly, apparatuses, systems and methods are provided for generating a vehicle driver signature based on current image data, previously classified image data, and vehicle dynamics data.

Abstract: An electronic device for detecting a target object is configured to obtain a plurality of first candidate boxes corresponding to a first object and a plurality of second candidate boxes corresponding to a second object by applying an image is provided. The electronic device includes the first object and the second object to an artificial intelligence model, wherein the artificial intelligence model is trained to use a loss function for reducing a size difference between candidate boxes corresponding to two adjacent objects to determine sizes of the candidate boxes.